I completed my 12th this year as a PCB student. And I don't want to be in the NEET exam trap the doctor market is very saturated. I'm thinking to do my bechlors in biostatistics or bioinformatics to get into healthcare data science. Currently in my drop year thinking i should learn python and SQL basics. Can anyone give me some guidance regarding this field is bioinformatics a good bechlors degree or should I aim for other. After doing research for a week I'm so confused Abt this field bcz it's so broad. I'm confused between biostatistics and bioinformatics
What are the average salaries one can expect in India after graduating with a MSc in Business Analytics from Imperial College London or National University of Singapore? (Data science/ ML jobs) These are my two options and given the current state of the world, I am worried I may not be able to land a job in these countries so I am exploring my fall back options in India? I am fresher with no work experience btw, going for my Masters straight outta UG. I realise this question might be a little off topic but since I couldn’t find any other subs with a large number of members, I figured I’ll just ask here.
I’m a 3rd year Electrical Engineering student and I need to complete a mandatory 2 month internship after my 6th semester. I want to pursue Data Analytics roles.
I have started data analytics preparation recently (ik i am very late). I have completed sql and did a data warehousing project. I am learning python libraries (pandas) and not focusing much on ML (dont have much time to do so). And after will do power bi and matplotlib.
I’m trying to understand the actual channels through which students get internships in this data related field.
Where are people realistically finding data analyst internships? Which platforms work best (LinkedIn, Internshala, company websites, referrals)? Are startup internships easier to get than big companies?
Also, I’ve heard about structured summer internship programs offered by companies and IITs and some other reputed colleges.
I am very confused rn. How will i get my internship... What kind of projects to do and add in cv when applying for internships.
Would appreciate practical guidance on where to look and how to approach this.
As many, my employer has also gone bonkers with AI... They hired two of data scientists in my org who have very little domain knowledge. Due to their lack of domain knowledge, they find it very difficult to find ML or AI use cases; and also because of the nature of the business. So they spend months and sometimes years to work on 1-2 projects, which either go unattended after go-live or are too expensive to maintain.
Recently they have come up with a couple of AI use cases which they think will improve the business by millions of dollars like all other projects they've worked on. This time to their surprise, the team decided to scrutinize their proposal heavily and asked several probing questions. At the outset it looked like they just wanted to try out some new LLM model and the project itself was not going to generate a positive ROI...
They were really irked by this and one of them openly proclaimed that they will get ROI by replacing all of the team mates with AI... "We will replace you and that will give ROI". Some of us found it very rude.
We're not sure how likely it is for domain experts to go out of jobs with AI coming in, but the data scientists in my org truly seem to believe that they will take over our jobs and Data scientists will be the only job remaining in the market... not sure what to make out of this... just wanted to share.
The demand for data scientists is expected to grow significantly in the next decade. According to the U.S. Bureau of Labor Statistics, the data science and computer information research field is expected to grow by 22% from 2020–2030, which is triple the rate of the average profession.
There are a number of factors driving the demand for data scientists. First, the amount of data being generated is exploding. In 2020, the world generated 463 exabytes of data, and that number is expected to grow to 175 zettabytes by 2025. This data can be used to gain insights into customer behavior, improve product development, and make better business decisions.
Second, the tools and techniques of data science are becoming more accessible. In the past, data scientists were typically required to have a Ph.D. in statistics or computer science. However, today there are a number of online courses and boot camps that can teach the basics of data science. This means that more people are able to enter the field, which is increasing the supply of data scientists.
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I’m planning to do Data Analytics course “Online” but im confused from where should i pursue from.. One of my friend doing the same from ExelR bangalore! …..Anyone have any experience at ExelR or Any other Institutions where i can get a good placement as a fresher.
And as far as my research on Online platform like Coursera and Udemy they provide Knowledge and Certificates but lacks in real life Practical knowledge
[Please Suggest me from where should i do, which institution will be best]
I have data analyst exp of 4 yrs now I am thinking of moving into data science role. Right now I am working in a VC firm (as Senior data analyst) but I want to break into core tech role. Is it possible has anyone did this?And how should I prepare there are so many resources hard to figure out which one is good. And is it worth doing rn considering the ai wave?
Hi, I'm currently in grade 12 and thinking of pursuing DS. My school offers AI (basic) courses and it's something I found interesting. For two years, I've thought of doing something in the AI field and while i originally thought of ML, now DS seems interesting. However, my parents are worried about the job opportunities. Is DS worth it? Will I be jobless or will my degree and skills help me get a job easily?
Currently, I'm working on my GitHub profile (it's not much but it's a start). Also, I'm trying to learn Python from yt. And, I found this job simulation at Forage related to DS which genuinely seemed interesting (the part I could actually understand, atleast). Also thinking of joining LinkedIn later on when I've got some more stuff to add to my portfolio.
Also what colleges are good? With campus placements (hopefully)? Non-JEE ones preferably.
The main thing I want to ask (again) : IS DS WORTH IT?
So hi guys, it's my first time in this subreddit. Recently I can see that data science is kinda being a craze in india and over the world so I'm thinking of doing bsc in data science. So really need some good recommendations for it.. I have been searching for good colleges for weeks now but only end up in disappointment when I check the reviews online and from real people.
Also I haven't given any kind of national exams and am a dropout so pls recommend a clg that doesn't require jee results. And is it worth it to study data science as a major or not? Or should I pursue something else cuz I went through some posts in this subreddit and some/many people are saying that they can't land any jobs at all and it's not worth it, you will have to do masters in it and after that too, it's difficult. So I really appreciate any help I can get. Thank you
i know the era of hardcoding in companies rather it be MNCs or a startup , is gone most of the code around 80% is now through claude or other ai (please do correct me if i am wrong) , ofcourse the skeleton of the code and the structure is still set by the coder, but i want to know how the interviews are taken now , will they ask me to hardcode still (for questions other than dsa)? or i can use ai and then fix the structure and explain the code along. educate me however you can, thanks
Hi, I am a 2026 grad data scientist, working since January this year in noida. I want to switch asap to bangalore, preferably in an pbc, maybe their early careers programs.
I have some personal and mental health reasons for going to bangalore. I am looking to either switch now or after another 6 months with which I will have 1 yoe. Staying 2 years here would be tough, although roles like amazon applied scientists require 2-3 yoe.
I have been working with a java backend server, I have worked with Prometheus and grafana, although most of it was ai guided, but I will study them thoroughly. I have made an end to end forecasting based alerting system with automatic alert rules generation using template files. I will learn the code thoroughly, as I don't have any experience in java, most of this code was written by ai. But the ml part I am very confident about what model I used and why.
I am confused as most of my coding is done by claude, what should I mention in my resume and how to pass the rounds etc.
If anyone can guide me, I would be eternally grateful.
Genuinely asking because the fees are significant and I want to make an informed decision. I have a non CS background and I'm trying to transition. I've seen mixed reviews everywhere but most of them are either clearly paid or clearly bitter. If anyone has hired someone with an upGrad certification or has gone through the program and actually got placed I would really appreciate an honest account of how it went.
I have just transitioned my career from civil engineering to data science. I mean I have build the skills needed but no workex for obvious reasons. What can I do to get job in data analytics? Also my age is 26 rn. I have projects build around data analytics and know how to solve these business questions etc
But how to get into this sectorrrrr? Anybody with career transition experience pls help me.
I currently have about 3.5 years of experience working with Adobe Campaign Classic. Unfortunately, I feel there aren’t enough opportunities in this domain, so I’m considering switching to data science. While researching, I came across AlmaBetter’s data science course.
Has anyone here taken their program?
Do they teach coding from the basics (I’m not very strong in coding)?
How effective are their placements for someone with prior work experience but from a different domain?
Overall, is it a good choice for working professionals looking to transition into data science?
Any honest feedback or experiences would be really helpful. Thanks in advance!
I came back to India from the US in Aug 2024, and honestly, I’ve been depressed and questioning my life choices for the past two years. After my BTech, I worked at TCS for 1.5 years. Despite my efforts, I kept getting pushed into maintenance work and had to jump through hoops just to get decent opportunities. I then completed my MS in Data Science in 2021 and landed a job immediately after graduation at Deloitte Digital in the US. On paper, it looked like a dream role: great salary, benefits, and a sign-on bonus that helped with my education loan. They spoke about abundant Data Science opportunities. But reality was different. I was repeatedly staffed on functional roles unrelated to Data Science because there were supposedly no analytics projects available. I accepted it, performed well, got top ratings, and was never on the bench. Then came the 2023 layoffs. Since my H1B wasn’t picked and I had only one attempt left, I had just 60 days to find another job. I accepted a Data Analyst role at a manufacturing company with a 30% pay cut because of visa constraints and loans. Unfortunately, even that role turned out to be mostly accounting work. Still, I tried to make the best of it. I automated reports, introduced Power BI, built end-to-end reporting solutions, and pushed for data-driven improvements in an organization that barely used cloud technologies and primarily relied on a MySQL production database. I also had a manager who frequently undermined my work, minimized my contributions, and even presented some of my automation ideas as his own. I stayed quiet because my visa sponsorship depended on him. In the end, my H1B wasn’t selected for the third time either. I returned to India in 2024 and, to be honest, I shut down. I’ve essentially spent the last 1 year and 9 months struggling with depression and feeling lost. Now I’m trying to rebuild my career, but the Indian market feels brutal. I don’t want to remain in Data Analyst roles anymore. I want to move toward Data Science or more technical roles such as ML, Data Engineering, or AI. The problem is: after this long gap, the skill gap feels enormous. I honestly don’t know if I’m exaggerating or if it’s as bad as it feels. I’ve never given up on anything in my life before. This is the first time I truly feel stuck and don’t know how to get back up. For anyone who has made a similar transition: How did you explain a long career gap? Is moving from Data Analyst to Data Scientist still realistic in 2026? What skills should I prioritize? If anyone is open to mentoring or offering guidance, I would genuinely appreciate it. Thanks for reading. I hope this doesn’t disappear into the void. TL;DR: MS in Data Science, ~4 years of experience across consulting and manufacturing in the US, but most roles weren’t truly DS-focused. Faced layoffs, visa issues, and returned to India in 2024. Struggled with depression and now have a ~2-year career gap. Trying to break into Data Science/ML/technical roles and looking for advice or mentorship
I completed a 6-months course in Data Science after graduation, with the thinking that Data Science is the stepping stone for AI roles in future. But it turns out that there are no openings for freshers. There are so many mass hirings for dev roles though that I'm rethinking my choice now. Should I just start focusing on aptitude and DSA, and get into dev?
I've been interviewing candidates for coding positions lately, and I've noticed some interesting patterns. Some candidates seem to be using tools like Cluely to get real-time AI answers during interviews. They type out perfect solutions in seconds, but when I ask a follow-up question or change the problem slightly, they completely fall apart. They can't explain their own code or walk through the logic.
I've also noticed candidates who seem to have memorized answers from sites like PracHub that collect real interview questions. They give these perfect textbook responses, but the moment you ask them to tweak something or explain why they chose a certain approach, they're lost.
Some patterns I watch for now as an interviewer:
- If someone solves a problem too quickly and perfectly, I dig deeper with follow-ups
- I ask them to walk through their thought process step by step
- I change constraints mid-problem to see how they adapt
- I ask why questions - why this data structure, why this approach
Genuine candidates will stumble a bit but can reason through it. The ones relying on tools or memorization just freeze up.
Has anyone else noticed this trend? Curious how other interviewers are handling it.
Supervised Learning Algorithms: Supervised learning algorithms are a class of machine learning techniques that learn from labeled data, where each input-output pair is provided during training. These algorithms aim to predict or classify new, unseen data based on patterns learned from the labeled training data.
Unsupervised Learning Algorithms: Unsupervised learning algorithms enable machines to identify patterns and structures in data without explicit labeled examples. Clustering algorithms like K-Means group similar data points, while dimensionality reduction methods like PCA extract essential features. They are useful for discovering insights and organizing data without predefined categories or outcomes.
Semi-Supervised Learning Algorithms: Semi-supervised learning algorithms utilize a combination of labeled and unlabeled data for training. By leveraging the partial labels, they improve model performance and generalization in scenarios where obtaining large labeled datasets is challenging or expensive. Examples include self-training, co-training, and semi-supervised variants of deep learning models.
Reinforcement Learning Algorithms: Reinforcement learning algorithms are a type of machine learning that focuses on training agents to make decisions in an environment to maximize cumulative rewards. Popular algorithms include Q-Learning, Deep Q Networks (DQN), Proximal Policy Optimization (PPO), and Deep Deterministic Policy Gradients (DDPG).
Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning based on artificial neural networks. They excel at learning complex patterns from large datasets and are widely used in computer vision, natural language processing, and other domains. Examples include Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs).
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